Time series are data observed over time (either in continuous time or at discrete time periods).

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Division with lag operator

I have a basic question about lag operators, but can't seem to find the answer online anywhere. If I have an equation like so: $ (1-L)P = XY(1-L) $ can I divide out the (1-L) from each side and just ...
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62 views

Parametric vs non-parametric time series modeling

Hi I have a large data set of objects, each containing a set of the same attributes. The attributes are measured quantities like height, width, etc. The data is arranged in a time series so that the ...
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53 views

Time series analysis (removing effect of external factor)

I'm currently working on detecting the response from a sensor with the following profile: The sensor responds to temperature fluctuations and I was wondering if y'all could suggest methods to ...
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22 views

Applications of time series outside finance

What are some interesting application of time series outside econometrics and finance? The wikipedia article didn't help much :)
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How are the ARIMA forecasted values calculated?

If I have data for 10 days and want to predict the data for the next day based on the previous ones, how does ARIMA find the predicted value? Is it just the average of the last 10 days, or are other ...
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27 views

Data forecasting; which method should I use?

I am interested in predicting the data for a day, based on the data given from the 14 previous days. The data I am working with is the number of subscriptions to a website per day. Each day, the ...
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38 views

Stationarity in ARIMA modeling

I am working on a problem that I think ARIMA modeling could be useful for, and am researching the theory behind ARIMA. I came across this website that says: ARIMA(p,d,q) forecasting equation: ARIMA ...
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comparing arima(1,0,0) model to lm produces very similar ar(1) coefficients and very different intercepts

I'm diving into arima models and was trying to repreduce the results of auto regression. here is a reproducable example: ...
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41 views

Serially Correlated Regressors

I am trying to find information (without success) regarding serially correlated regressors in linear time series regression setting. The topics covered are either correlation between regressors, or ...
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80 views

Why time series analysis is not considered a machine learning algorithm

Why is time series analysis not considered a machine learning algorithm ( unlike linear regression). Both regression and time series analysis are forecasting methods. So why is one of them considered ...
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68 views

Analyzing seasonality in data

In order to analyze the data in presence of seasonality, I used two methods: Proportional hazard model (Cox model) and time series method (Triple Exponential Smoothing (Holt Winters Method)). Now , my ...
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32 views

How I can get best arima model in R? [closed]

I would like to build a time series model for univaraite data in order to predict or forcast. I am a bit new to R but know some of the syntax. More over, I would like to get the best arima model with ...
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18 views

Prediction of one time series from another with multi-dimensional predictors

I have the following problem (I am a newbie to the field so my apologies if this is something standard). I have two time-series with the same (categorical) predictors (x1,...,xn), n is approximately ...
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Causal form coefficients from AR(2) model

I'm following Shumway & Stoffer's "Time SEries Analysis and Its Applications With R Examples: EZ -- Third Edition". At the top of page 86, an AR(2) forecasting model $ x^n_{n+m} = 6.80 + 1.35 ...
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32 views

Outlier detection with ARIMA models?

I have several different time series with monthly values for 8 years, where I fit an ARIMA model. And the purpose is to forecast the next year and indicate possible outliers in a fancy way. Is the ...
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38 views

How does svm deal with new levels of a variable added over time when considering time series data?

I am trying to predict customer spending for an X year period after t0. I train an svm model with transactions occurring before and on t0, on the cumulative spending of the customers after t0. I then ...
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39 views

ARIMA models for mortality modelling (Box-Jenkins methodology)

Fitting the Lee-Carter model of mortality to data provides a time series for the period-related effect, which is subsequently often modelled as an ARIMA(p,d,q) process in order to make forecasts. p,d ...
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59 views

How to deal with a single Yearly spike with ARIMA?

I have a time series which shows an yearly spike around summer but otherwise is predictable by an AR(1) model. The tests on the data also show that the time series shows stationarity and is ...
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A closed form formula for the normalizing constant in standard normal auto-regressive series?

Let $Z_t = c_1Z_{t-1} + c_2Z_{t-2} + ... + c_nZ_{t-n} + c\epsilon_t$ where $Z_t, \epsilon_t \sim \mathtt{N}(0,1)$ are iid variables and $Z_s \sim \mathtt{N}(0,1)$ for all $s$. Given the values of ...
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32 views

Backtesting in neural network field

I'm new to the neural network field and I would like to understand how one can backtest a neural network trained with backpropagation methodology. Particularly, I have a time series dataset and I ...
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37 views

spurious regression/co-integration

I have two I(1) time series and I regressed one against the other and found that it had low to moderate R-squared but my DW statistic is about 0.015. I know the literature says this is the case of ...
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multiple, related, time-series applied to Statistical Process Control

I tried looking online (google), searching in stack-overflow and cross-validated, and just looking through "R" documentation for the answer, but either I am not seeing it, or I don't know how to tell ...
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34 views

P-value of Augmented Dickey-Fuller test and KPSS test

I would like to test if the time series of the US 3-month treasury bills (monthly data from 1934 to 2015) is stationary. I'm using the ADF test in R (from the package ...
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Difference between Time delayed neural networks and Recurrent neural networks

I would like to use a Neural Network to predict financial time series. I come from an IT background and have some knowledge of Neural Networks and I have been reading about these: TDNN RNN I have ...
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Time dependence and cumulative logit regression question

I'm looking to do some research with the GSS (the General Social Survey; a survey that asks over a 1000 people every year various questions and collects their demographic information as well). I'd ...
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36 views

Interpretation of coefficients in linear regression: first difference of logs

what is the correct interpretation of coefficients in time series regression when using first differencing on logs of the DV and in certain IVs. FD(lnY)=c+beta1*ln(X)+beta2*FD(lnZ) The log ...
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Strictly Stationary Time Series with Infinite Moments

Can someone give me an example of a strictly stationary time series with infinite moments? I am reading a book on Time Series by Wayne A. Fuller where it is said that a strictly stationary time series ...
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Recommended Study Area For Processes

I am looking for a machine learning area that deals in processes for logistics. If anyone can show me some use cases or even point me in the direction of a couple of algorthims. Im currently using R ...
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25 views

What are some good (and fast) alternatives to dynamic time warping?

I am planning to cluster tens of thousands of time series of different lengths into two groups.
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Hierarchy predictive top down approach

I'm having a problem with using a hierarchical top down forecasting approach. According to my understanding, when I split an aggregated value on the levels below it, I have to know the percentages ...
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107 views

ARMA-GARCH model selection / fit evaluation

I'm trying to fit an ARMA-GARCH model to a data set of FTSE 100 log returns (which I've uploaded here). However, I'm not able to find a well-fitting model. Below are the ACF and PACF of the log return ...
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62 views

Properly calculate mean and SE for meta-analysis

I want to perform a meta-analysis, for which I have data from 30 different experiments, each of them with two different treatments. I have three different types experiments based on how the data are ...
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23 views

How do I measure the significance of a trend [closed]

I have a 5 year dataset by month, so I have 69 discrete measures of the same value. That value shows an increasing trend, but I'm unsure how to measure it's significance. My team would also like a p ...
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98 views

What does the following ACF curve mean ? (Picture attached)

I was checking for seasonality and other dependencies and this is the curve I get . There's no apparent seasonality....but what exctly does the falling slope mean? Any help would be appreciated. ...
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Cointegration in R - Standard error, test statistic and p-value of weights

I'm using urca package in R version 3.2.1. I used ca.jo function on a set of I(1) regular time series variables - taking two at ...
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29 views

Correlation coefficients of a time series

I have 100 simulations of an ARMA(1,2) process, created with R is such a way: ...
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28 views

Time-series cross-sectional classification problem

I have a time-series cross-sectional dataset consisting of 100 individuals that each had 4 features measured yearly for 21 consecutive years. One of the features is binary and the other three are ...
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Statistical test for increasing incidence of a rare event

I have following simulated data of 2500 persons regarding the incidence of a rare disease over 20 years ...
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Poisson vs. Gaussian in Geomagnetic Data

I've been studying geomagnetic signals using a threshold approach to detect pulse events in the data. The question here is what is the significance of the crossover of stddev and mean as the ...
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Why does the variance of the Random walk increase?

The random walk that is defined as $Y_{t} = Y_{t-1} + e_t$, where $e_t$ is white noise. Denotes that the current position is the sum of the previous position + an unpredicted term. You can prove that ...
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Coherence vs. Magnitude Squared Coherence

Currently I am writing my master thesis. The theory part is about the turbulent wind field generation, where the coherence (not magnitude squared) is used: $$\text{coh}(f) = ...
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Mixed effects model for repeated measures to test for factors that are either constant or dynamic within an individual over time

I am dealing with a rather complicated dataset with repeated measures of the same individuals at various time points (samples were collected at different time points and different number of samples ...
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28 views

Simulation of time series with R [closed]

I'm new on time series. I'm trying to solve an exercise on the simulation of an ARMA process. The problem is the following: Generate 100 simulations, each with n=60 elements of an ARMA(1,2) process ...
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40 views

How to test whether a random walk have a upward or downward trend

I have applied Kalman Filtering Method to get the estimates for time-varying coefficients (using DLM package in R). The model is like $S_{t} = \alpha_t * A_t + \beta_t * W_t + \gamma_t * A_t * W_t + ...
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non-invertible weakly stationary AR processes

ok, so I know (for example) that $X_t = 2X_{t-1} + e_t$ for iid $e_t$ is stationary. But how do I go about proving the condition $Cov(X_r, X_s) = Cov(X_{r+t}, X_{s+t})$ for all integer $r, s, t$ ...
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21 views

Detrending a series that moves about zero

How can I avoid my de-trending model from blowing up? Do I need an additive model rather than a multiplicative model? If so, is there anything I would consequently need to take into account? Can I ...
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9 views

Can I run an SVM on sparse temporal data without a regular time interval?

I have data of occurrences with timestamps that could be days or months apart. I'd like to enter the values natively as follows. Are there any SVM algorithms that can support such an input? day 1: ...
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Vector Autoregressive Model: residual's kurtosis proportional to number of lags?

I have some transformed data set (windspeeds that are nearly-weibull-distributed). I transformed this data which results in near-normal distribution (close to no excess kurtosis and skewness of zero). ...
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How can I recreate a Weibull distribution given mean and standard deviation and the shape and scale parameters are unknown?

Figure 2 is a Weibull distribution of three different wind farms in Canada. These 3 probability distributions were combined in a study to obtain a common wind speed model. I will be using this common ...
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How to detect variable seasonality pattern

We have predicted and actual (daily) data for past 3 years. We use 90 days of data for prediction. Generally our predictions are very accurate, but we receive unusual traffic for few days/weeks ( like ...